Speech recognition systems are increasingly being used to translate human spoken words or utterances directly into its written equivalent and meaning. Speech recognition systems can avoid the need for these spoken words to be manually entered into a computer, or to be recognized by a human. Therefore, speech recognition systems are desirable for many businesses because they allow employees to perform other tasks.
One example of how a business can use a speech recognition system is for retrieving the identity of a caller over a telephone. Instead of requiring an employee to ask for and receive the caller's identification, known speech recognition systems can ask for a preassigned caller identifier and, using speech recognition techniques, translate the caller's utterance into the recognized identifier. The recognized identifier can then be used to perform a transaction.
However, one problem with known speech recognition systems is that they typically are not very accurate. Most speech recognition systems are likely to present multiple possible identifiers, sometimes referred to as "N-best" choices, one at a time to the caller in response to the caller's utterance. For each possible presented identifier, the caller must respond as to whether it is the correct identifier of the caller or not. As more and more incorrect identifiers are presented to the caller, the caller is likely to get frustrated and angry. If the caller is a customer or a potential customer of the business, the business may be reluctant to use the speech recognition system in order to avoid angering or frustrating a customer, especially a highly valued customer.
Based on the foregoing, there is a need for an improved speech recognition system for retrieving a customer identifier that is more accurate and/or is less likely to anger or frustrate highly valued customers.